Octopamine receptors (OARs) perform key operations in the biological pathways of invertebrates only, making this class of G-Protein Coupled Receptors (GPCRs) a potentially good target for insecticides. However, the lack of structural and experimental data for this insect-essential GPCR class has promoted the development of homology models that are good representations of their biological equivalents and will thus be useful in the development of an insecticide. I report here the functional characterization of two Anopheles gambiae OARs and the discovery of new OAR agonists and antagonists based on virtual screening and Molecular Dynamics (MD) simulations. Experimental validation of the results shows the accuracy of the model. Supporting prior GPCR studies, Asp100 in the TM3 region, and Ser206 and Ser210 in the TM5 region were found to be important to the activation of the protein. The current combined computational and experimental approach seems appropriate for creating and refining homology models of the octopamine receptor and in turn aid in the discovery of new and effective insecticides.Improvements can be made to the virtual screening procedure by allowing the protein to fully assume its active and inactive states. However, GPCRs are large proteins, are suspended in a lipid bilayer, and are activated on the millisecond timescale, all of which make conventional Molecular Dynamics (MD) simulations infeasible, even if run on large supercomputers. However, accelerated Molecular Dynamics (aMD) simulations can reduce this timescale to even hundreds of nanoseconds, while running the simulations on Graphics Processing Units (GPUs) would enable even small clusters of GPUs to have processing power equivalent to hundreds of CPUs. My results show that aMD simulations run on GPUs can successfully obtain the active and inactive state conformations of a GPCR on this reduced timescale. Furthermore, I discovered a potential alternate active-state agonist-binding position in the octopamine receptor which has yet to be observed and may be a novel GPCR agonist-binding position. These results demonstrate that a complex biological system with an activation process on the millisecond timescale can be successfully simulated on the nanosecond timescale using a simple computing system consisting of a small number of GPUs.